Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles

Quasi-SMILES as a basis to build up models of endpoints for nanomaterials

Simplified molecular input-line entry system (SMILES) is a format for representing of the molecular structure. Quasi-SMILES is an extended format for representing molecular structure data and some eclectic data, which in principle could be applied to improve a model's predictive potential. Nano-quantitative structure-property relationships (nano-QSPRs) for energy gap (Eg, eV) of the metals oxide nanoparticles based on the quasi-SMILES give a predictive model for Eg, characterized by the following statistical quality for external validation set n = 22, R2 = 0.83, RMSE = 0.267.

Chemometrics-assisted mechanism study of the room-temperature phosphorescence on nanoscopic boronate assemblies

Boronate assemblies, derived from benzene-1,4-diboronic acid, showed room-temperature phosphorescence (RTP) in both the solid-state and dispersions, being quite sensitive to the preparation conditions. Our chemometrics-assisted quantitative structure-property relationship (QSPR) analysis between the nanostructure and RTP behavior of boronate assemblies led us not only to understand their RTP mechanism but also to predict the RTP properties of unknown assemblies from the PXRD patterns.

Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment

Basic principles and problems of the systematization of data on nanomaterials are discussed. The eclectic character of nanomaterials is defined as the key difference between nanomaterials and traditional substances. The quasi-SMILES technique is described and discussed. The possible role of the approach is bridging between experimentalists and developers of models for endpoints related to nanomaterials. The use of models on the possible impact of nanomaterials on the environment and human health has been collected and compared. The new criteria of the predictive potential for the above models are discussed. The advantage of the statistical criteria sensitive simultaneously to both the correlation coefficient and the root mean square error noted. The rejection of the border between the effect of the biochemical reality of substances at a molecular level and the effect of experiment conditions at the macro level gives the possibility to develop models that are epistemologically more reliable in the comparison with traditional models based exclusively on the molecular structure-biological activity interdependence (without taking into account experimental conditions). Models of the physicochemical and biochemical behaviour of nanomaterials are necessary in order to develop and apply new industrial achievements, everyday comfort species, medicine, cosmetics, and foods without negative effects on ecology and human health. The CORAL (abbreviation CORrelation And Logic) software provides the user with the possibility to build up nano-QSAR models as a mathematical function of so-called correlation weights of fragments of quasi-SMILES. These models are built up via the Monte Carlo method. Apparently, the quasi-SMILES is a universal representation of nano-reality since there is no limitation to choose the list of eclectic data able to have an impact on nano-phenomena. This paradigm is a convenient language to the conversation of experimentalists and developers of models for nano-phenomena.

Metal Sulfide Nanoparticle Synthesis with Ionic Liquids - State of the Art and Future Perspectives

Metal sulfides are among the most promising materials for a wide variety of technologically relevant applications ranging from energy to environment and beyond. Incidentally, ionic liquids (ILs) have been among the top research subjects for the same applications and also for inorganic materials synthesis. As a result, the exploitation of the peculiar properties of ILs for metal sulfide synthesis could provide attractive new avenues for the generation of new, highly specific metal sulfides for numerous applications. This article therefore describes current developments in metal sulfide nanoparticle synthesis as exemplified by a number of highlight examples. Moreover, the article demonstrates how ILs have been used in metal sulfide synthesis and discusses the benefits of using ILs over more traditional approaches. Finally, the article demonstrates some technological challenges and how ILs could be used to further advance the production and specific property engineering of metal sulfide nanomaterials, again based on a number of selected examples.

Bandgaps of noble and transition metal/ZIF-8 electro/catalysts: a computational study

Zeolitic imidazolate frameworks (ZIFs) are designed with metals as center atoms, connected by imidazole-like linkers. The created structures have been employed considerably in the field of advanced energy materials, including catalysis/electrocatalysis and energy storage and harvesting applications. In the present study, the bandgaps of pristine and doped ZIF-8 (using noble and transition metal dopants such as Pd, Pt, Ni, Mn, Co, Cu, Fe, and Ti) are determined. This can result in a promising approach to enhance the corresponding electronic properties while applying noble metal-free dopants. To determine the bandgap values, a quantum mechanical modeling based on density functional theory (DFT) was applied. Then, due to the time-consuming and complicated nature of this approach, the obtained results from the DFT study were then employed to develop the support vector machine (SVM) model to estimate the bandgap of the resulting nanostructure. The outcomes of the proposed model showed its high accuracy, with R² of 0.98 and root mean squared error (RMSE) of 0.04. The developed model could have great value in designing various ZIF-8-based nanostructures, particularly when applied in electro/catalytic reactions, e.g., electrocatalytic hydrogen evolution reaction or catalytic hydrogenation reaction, through a simple approach.

Band gap estimation for metal/ZIF-8 framework electro/catalysts by hybrid DFT and machine learning technique.